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1.th IEEE International Conference on Intelligent software Methodologies, Tools and Techniques, SoMeT 201.

作者： Nemeth, Bottyan Takacs, Gabor Pilaszy, Istvan Tikk, Domonkos Gravity Research and Development Zrt. Bálint M. u. 64. Gyor Hungary Dept. of Mathematics and Computer Science Széchenyi István University Egyetem tér 1. Gyor Hungary Óbuda University Software Engineering Institute Bécsi út 96/b Budapest Hungary

ISBN: (纸本)9781479904211

In this paper we will describe a modification of the matrix factorization (MF) algorithm which allows visualizing the user and item characteristics. When applying MF for collaborative filtering, we get a model that represents the attributes of users and items by feature vectors. Some elements of these vectors may have understandable meaning for humans but due to the lack of internal connections between the feature vectors, these are difficult to visualize. In this paper we give a detailed description of a MF method enabling better visualization of features by arranging them into a 2D map, where via the calculation of the feature values we try to position features with similar 'meaning' close to each other. To achieve this first we define a neighborhood relation on features, then we modify the MF so that we introduce a new term in the error function which penalize the difference between the neighbor features. We show that this modification slightly decrease the accuracy of the model but we get well visualized feature maps. On the feature maps meanings can be associated with regions, and so we can provide an interesting explanation for the user why he/she was recommended the movie. Such plausible explanations may result in that users will better understand how the system works, which can also increase customer loyalty towards the service provider. © 201. IEEE.

关键词： Collaborative filtering

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Security challenges in IoT development: A software {1. perspective 17

Security challenges in IoT development: A software engineeri...

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201. XP Scientific Workshops, XP 201.

作者： Duc, Anh Nguyen Jabangwe, Ronald Paul, Pangkaj Abrahamsson, Pekka NTNU Trondheim Sør-TrøndelagNO-7491 Norway Lero - The Irish Software Research Centre Regulated Software Research Centre Dundalk Institute of Technology Dundalk Co.Louth Ireland Department of Business Administration and Computer Science University College of Southeast Norway Bø i Telemark Norway Software Engineering Research Lab Sweden Blekinge Institute of Technology Karlskrona Sweden STATSports International 1 Courtney Hill Newry Co. Down Ireland

ISBN: (纸本)9781450352642

The rapid growth of Internet-of-things (IoT) software applications has driven both practitioners and researchers' attention to methodological approaches for secure IoT development. Security issues for IoT is special in the way that they include not only software, but also hardware and network concerns. With the aim at proposing a methodological approach for secure IoT application development, we investigated what are security challenges in the context of IoT development. We reviewed literature and investigated two industry cases. The preliminary finding results in a list of 1. security challenges with regards to technical, organizational and methodological perspectives. Cross-case comparison provides initial explanation about the less emphasis on methodological and organizational security concerns in our cases. © 201. ACM.

关键词： Internet of things

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Analyzing and Comparing Results for Multiple Machine Learning Models 4

Analyzing and Comparing Results for Multiple Machine Learnin...

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2023 4th International Conference on computer {1. and Intelligent Control, ICCEIC 2023

作者： Ng, Jefferson Li, Yijian Ji, Xuxiao Yang, Can Wan, Jiaqiao Lu, Xingyu University of California Department of Mechanical Engineering Irvine92697 United States Courant Institute of Mathematical Sciences New York University NY10012 United States University of Waterloo School of Computer Science WaterlooCAN2L 3G1 United States Oregon State University Department of Electrical Engineering and Computer Science Corvallis97331 United States Zhengzhou University Software College Zhengzhou450000 China United World College Changshu China Jiangsu215500 China

ISBN: (纸本)9798350308877

In the contemporary landscape of atmospheric sciences, the ability to recognize specific weather phenomena accurately and efficiently has become paramount, largely due to the exponential growth in sensor-generated data. This paper undertakes an in-depth analysis of advanced algorithms, namely YOLOV8, ResNet50, and Convolutional Neural Networks (CNN), for the purpose of weather pattern identification and classification. Building on the foundation of deep learning, the YOLOV8's real-time object detection capabilities are leveraged to discern intricate weather patterns in diverse datasets, from meteorological stations to satellite imagery. On the other hand, Resnet 50 which is another CNN based model was also tested along with CNN for comparison. An exhaustive evaluation of these algorithms covers various metrics, including accuracy, precision, computational efficiency, and real-world applicability. Special emphasis is placed on the crucial stages of data preprocessing, feature extraction, and model tuning, highlighting their impact on the algorithms' overall performance. Our findings suggest that, when appropriately optimized, both YOLOV8 and CNN exhibit exceptional capabilities in discerning and classifying intricate weather patterns, whereas ResNet50 exhibits comparatively less performance. © 2023 IEEE.

关键词： Convolutional neural networks

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Mobile data management: 4th International conference, MDM 2003 Melbourne, Australia, january 21.24, 2003 proceedings 4th

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4th International Conference on Mobile Data Management, MDM 2003

作者： Chen, Ming-Syan Chrysanthis, Panos K. Sloman, Morris Zaslavsky, Arkady National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei Taiwan University of Pittsburgh Dept. of Computer Science Sennott Square Building 210 S. Bouquet Street PittsburghPA United States Imperial College of Science Technology and Medicine Department of Computing 180 Queen’s Gate London United Kingdom Monash University School of Computer Science and Software Engineering 900 Dandenong Road Caulfield East MelbourneVIC Australia

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Enhancing the system development process performance: A value-based approach

Enhancing the system development process performance: A valu...

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22nd Annual International Symposium of the International Council on Systems engineering, INCOSE 201. and the 8th Biennial European Systems engineering Conference 201., EuSEC 201.

作者： Mastrofmi, Manuel Cantone, Giovanni Seaman, Carolyn Shull, Forrest Diep, Madeline Falessi, Davide University of Rome Tor Vergata Department of Computer Science and Systems and Production Via del Politecnico 1 00133 Rome Italy Fraunhofer Center for Experimental Software Engineering University of Maryland Baltimore County Department of Information Systems ITE. Building. 1000 Hilltop Circle Baltimore MD 21250 United States Fraunhofer Center for Experimental Software Engineering 5825 University Research Court Suite 1300 College Park MD 20740-3823 United States Simula Research Laboratory Oslo Norway University of Rome Tor Vergata Department of Computer Science Systems and Production Via del Politecnico 1 00133 Rome Italy

ISBN: (纸本)9781622769162

When planning or controlling the system development process, a project leader needs to make decisions which take into account a number of aspects, including: availability of assets and competences, previously enacted processes in the organization, certifications the system is required to obtain, standards to comply with, interactions among process activities, contextual factors and constraints, and allocated budget and schedule. In this paper we propose a value-based approach for supporting decision making. The aim is to provide supportive information for decisions related to the system verification process. This would in turn enhance the performance of system development process by supporting the decision making process for complex systems. We report both academic and industrial empirical evaluations, which demonstrate the feasibility and effectiveness of our proposal, and thus prompt us to refine and extend our approach to sub-processes other than verification. ©201. by the authors.

关键词： Decision making

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Joint Minimization of Wired and Wireless Traffic for Content Delivery by Multicast Pushing

Joint Minimization of Wired and Wireless Traffic for Content...

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作者： Chen, Zhao Tao, Xiaoming Jiang, Chunxiao Leung, Victor C.M. Beijing National Research Center for Information Science and Technology Department of Electronics Engineering Tsinghua University Beijing100084 China Beijing Innovation Center for Future Chip Tsinghua University Beijing100084 China Tsinghua Space Center Tsinghua University Beijing100084 China BNRist Tsinghua University Beijing100084 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

As more mobile users become subscribers of content services, their subscribed content can be directly pushed from the content provider into the user equipment after the content is generated. In current and future network paradigms, a joint wired and wireless transmission design for this pushing is needed to guarantee the user experience without the extra deployment of communication infrastructures or consumption of resources. In this paper, we investigate a joint wired and wireless content delivery system that incorporates wired and wireless multicast. The users in the same group are served by wireless multicast from a base station (BS), while the BSs of the same content form a multicast tree in a backbone wired network. The sum of wired and wireless traffic is minimized by a joint design of user grouping, subchannel allocation, wired routing, and wired link usage. Exploiting the monotonicity of wired and wireless traffic with regard to the wired hop count, the original problem is converted for searching the optimal hop count vector that achieves the minimum sum of both types of traffic, which is solved by a monotonic optimization (MO)-based iterative algorithm. Compared with existing schemes and according to the numerical results, a reduction in total traffic of 43% can be achieved by our approach. © 201. IEEE.

关键词： Iterative methods

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A Real-Time Approach to Recognition of Kazakh Sign Language

A Real-Time Approach to Recognition of Kazakh Sign Language

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Zholshiyeva, Lazzat Zhukabayeva, Tamara Turaev, Sherzod Berdiyeva, Meruyert Kuanysbaevna, Sengirbaeva Raykhan High School Of Information Technology And Engineering Astana International University Nur-Sultan Kazakhstan College Of Information Technology United Arab Emirates University Department Of Computer Science & Software Engineering Al Ain United Arab Emirates South Kazakhstan Medical Academy Department Of Medical Biophysics And It Shymkent Kazakhstan Special Boarding School №1 For Children With Hearing Impairment Shymkent Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The interaction between man and computer is expanded with computer technology development. Hand gesture recognition is an essential technology for human-computer interaction, and it allows the recording and interpretation of hand gestures without some devices' use. Since the hand is a man's primary working tool, its position and orientation in space are crucial for many potential applications, such as interaction with virtual reality devices. Hand position estimation is a substantial process of hand gesture recognition. However, there are many difficulties in 3D hand pose estimating from a single RGB image. Media Pipe is a high-quality finger and hand-tracking framework. It uses in machine learning to detect 3D landmarks. The proposed paper provides a real-time approach to recognizing Kazakh gestures performance using a conventional camera and Media Pipe. © 2022 IEEE.

关键词： Virtual reality

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Applications of Machine Learning for Face Mask Detection During COVID-1. Pandemic 2nd

Applications of Machine Learning for Face Mask Detection Dur...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Khan, Sarfraz Fayaz Khan, Mohammad Ahmar Al-Quadah, Rabiah SAT Algonguin College 1385 Woodroffe Ave OttawaONC211K2G1V8 Canada Department of MIS-CCBA Dhofar University Salalah Salalah Oman Department of Computer Science and Software Engineering Concordia University Mackay Street MontrealQC2070H3G 2J1 Canada

ISBN: (纸本)9789819934805

Covid-1. pandemic has forced us to adapt to the new lifestyle. World Health Organization (WHO) recommends that people should adhere to the public health expert’s guidelines to fight against the spread of Covid-1.. The most essential Covid-1. guideline has been the use of facemask which has been enforced throughout the globe and has proven to contain the spread of corona virus. The proposed study aims at examining the detection of mask usage by people through Machine learning approach. The research employs binary classification problem to detect and classify people wearing masks from the people not-wearing masks. Three machine learning models namely InceptionV3, VGGNet and Resnet have been adapted in this research for pre-processing the input images. Similarly, XGBoost, Random Forest and fully connected DNN models have been used for decoding and classification. Performance evaluation has also been done for the different models and a comparison of the performances has been carried out as a part of the research. The results obtained through performance evaluation technique showed that ResNet+Fully Connected DNN is the best among the developed models where the precision was 99.73%, accuracy was 99.7%, F1.Score was 99.69% and the recall score was 99.66%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-1.

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN computer {1. 2023年第4期4卷 363页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Multimodal interaction - Improving usability and efficiency in a mobile GIS context

Multimodal interaction - Improving usability and efficiency ...

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1.t International Conference on Advances in computer-Human Interaction, ACHI 2008

作者： Doyle, Julie Bertolotto, Michela Wilson, David School of Information Technology and Engineering University of Ottawa ON K1N6N5 Canada School of Computer Science and Informatics University College Dublin Belfield Dublin 4 Ireland Department of Software and Information Systems University of North Carolina at Charlotte 9201 University City Blvd. NC 28223 United States

ISBN: (纸本)0769530869

The context of mobility raises many issues for GIS applications. Mobile device limitations, including pen input whilst in motion, result in interfaces which are difficult to navigate and interact with. However, comparatively little research has been conducted to address the interface mobility problem for GIS. We are particularly concerned with the limited interaction techniques available to users of mobile GIS which play a primary role in contributing to the complexity of using such an application whilst mobile. Our research focuses on multimodal interfaces as a means to present users with a wider choice of modalities for interacting with GIS applications. The focus of this paper concerns a comprehensive user study which demonstrates the benefits, in terms of usability and efficiency, of a multimodal interface for the CoMPASS mobile GIS which we have developed. © 2008 IEEE.

关键词： Efficiency

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